Job Description
Are you ready to build the infrastructure of tomorrow? FutureCore Technologies is seeking a visionary Lead AI Architect for 2026 Systems to spearhead our next-generation artificial intelligence initiatives. We are not just predicting the future; we are architecting it.
In this pivotal role, you will bridge the gap between theoretical breakthroughs and scalable, real-world deployment. You will lead a team of elite engineers in designing neural networks and quantum-ready algorithms that will define the technological landscape of the late 2020s. If you are passionate about the future of AI and possess the technical prowess to execute complex systems at scale, we want to hear from you.
Why join FutureCore?
- Work on cutting-edge AGI (Artificial General Intelligence) projects.
- Competitive equity package and salary.
- Flexible remote-first culture with a hub in the heart of San Francisco.
Responsibilities
- Architectural Leadership: Design and oversee the development of scalable AI systems and infrastructure capable of handling petabyte-scale data processing for 2026 applications.
- Strategic Vision: Define the technical roadmap for AI capabilities, ensuring alignment with long-term business goals and industry trends.
- Team Mentorship: Lead, mentor, and cultivate a high-performance engineering team, fostering a culture of innovation and technical excellence.
- Model Optimization: Lead efforts in optimizing deep learning models for speed, accuracy, and efficiency on edge and cloud environments.
- Research Integration: Evaluate and integrate emerging research papers and technologies to maintain a competitive edge.
Qualifications
- Education: Master’s or PhD in Computer Science, Artificial Intelligence, or a related technical field (PhD preferred).
- Experience: 8+ years of experience in software engineering, with at least 5 years in a senior architecture or leadership role within AI/ML.
- Technical Skills: Proficiency in Python, TensorFlow, PyTorch, and distributed computing frameworks (e.g., Kubernetes, Apache Spark).
- Domain Knowledge: Deep understanding of Neural Networks, Natural Language Processing (NLP), and Reinforcement Learning.
- Communication: Exceptional ability to communicate complex technical concepts to non-technical stakeholders and cross-functional teams.